// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
#define EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H

namespace Eigen {

/** \class TensorGeneratorOp
 * \ingroup CXX11_Tensor_Module
 *
 * \brief Tensor generator class.
 *
 *
 */
namespace internal {
template<typename Generator, typename XprType>
struct traits<TensorGeneratorOp<Generator, XprType>> : public traits<XprType>
{
	typedef typename XprType::Scalar Scalar;
	typedef traits<XprType> XprTraits;
	typedef typename XprTraits::StorageKind StorageKind;
	typedef typename XprTraits::Index Index;
	typedef typename XprType::Nested Nested;
	typedef typename remove_reference<Nested>::type _Nested;
	static const int NumDimensions = XprTraits::NumDimensions;
	static const int Layout = XprTraits::Layout;
	typedef typename XprTraits::PointerType PointerType;
};

template<typename Generator, typename XprType>
struct eval<TensorGeneratorOp<Generator, XprType>, Eigen::Dense>
{
	typedef const TensorGeneratorOp<Generator, XprType>& type;
};

template<typename Generator, typename XprType>
struct nested<TensorGeneratorOp<Generator, XprType>, 1, typename eval<TensorGeneratorOp<Generator, XprType>>::type>
{
	typedef TensorGeneratorOp<Generator, XprType> type;
};

} // end namespace internal

template<typename Generator, typename XprType>
class TensorGeneratorOp : public TensorBase<TensorGeneratorOp<Generator, XprType>, ReadOnlyAccessors>
{
  public:
	typedef typename Eigen::internal::traits<TensorGeneratorOp>::Scalar Scalar;
	typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
	typedef typename XprType::CoeffReturnType CoeffReturnType;
	typedef typename Eigen::internal::nested<TensorGeneratorOp>::type Nested;
	typedef typename Eigen::internal::traits<TensorGeneratorOp>::StorageKind StorageKind;
	typedef typename Eigen::internal::traits<TensorGeneratorOp>::Index Index;

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorGeneratorOp(const XprType& expr, const Generator& generator)
		: m_xpr(expr)
		, m_generator(generator)
	{
	}

	EIGEN_DEVICE_FUNC
	const Generator& generator() const { return m_generator; }

	EIGEN_DEVICE_FUNC
	const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_xpr; }

  protected:
	typename XprType::Nested m_xpr;
	const Generator m_generator;
};

// Eval as rvalue
template<typename Generator, typename ArgType, typename Device>
struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device>
{
	typedef TensorGeneratorOp<Generator, ArgType> XprType;
	typedef typename XprType::Index Index;
	typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
	static const int NumDims = internal::array_size<Dimensions>::value;
	typedef typename XprType::Scalar Scalar;
	typedef typename XprType::CoeffReturnType CoeffReturnType;
	typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
	typedef StorageMemory<CoeffReturnType, Device> Storage;
	typedef typename Storage::Type EvaluatorPointerType;
	enum
	{
		IsAligned = false,
		PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
		BlockAccess = true,
		PreferBlockAccess = true,
		Layout = TensorEvaluator<ArgType, Device>::Layout,
		CoordAccess = false, // to be implemented
		RawAccess = false
	};

	typedef internal::TensorIntDivisor<Index> IndexDivisor;

	//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
	typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
	typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;

	typedef typename internal::TensorMaterializedBlock<CoeffReturnType, NumDims, Layout, Index> TensorBlock;
	//===--------------------------------------------------------------------===//

	EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
		: m_device(device)
		, m_generator(op.generator())
	{
		TensorEvaluator<ArgType, Device> argImpl(op.expression(), device);
		m_dimensions = argImpl.dimensions();

		if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
			m_strides[0] = 1;
			EIGEN_UNROLL_LOOP
			for (int i = 1; i < NumDims; ++i) {
				m_strides[i] = m_strides[i - 1] * m_dimensions[i - 1];
				if (m_strides[i] != 0)
					m_fast_strides[i] = IndexDivisor(m_strides[i]);
			}
		} else {
			m_strides[NumDims - 1] = 1;
			EIGEN_UNROLL_LOOP
			for (int i = NumDims - 2; i >= 0; --i) {
				m_strides[i] = m_strides[i + 1] * m_dimensions[i + 1];
				if (m_strides[i] != 0)
					m_fast_strides[i] = IndexDivisor(m_strides[i]);
			}
		}
	}

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

	EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) { return true; }
	EIGEN_STRONG_INLINE void cleanup() {}

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
	{
		array<Index, NumDims> coords;
		extract_coordinates(index, coords);
		return m_generator(coords);
	}

	template<int LoadMode>
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
	{
		const int packetSize = PacketType<CoeffReturnType, Device>::size;
		EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
		eigen_assert(index + packetSize - 1 < dimensions().TotalSize());

		EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
		for (int i = 0; i < packetSize; ++i) {
			values[i] = coeff(index + i);
		}
		PacketReturnType rslt = internal::pload<PacketReturnType>(values);
		return rslt;
	}

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE internal::TensorBlockResourceRequirements getResourceRequirements() const
	{
		const size_t target_size = m_device.firstLevelCacheSize();
		// TODO(ezhulenev): Generator should have a cost.
		return internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size);
	}

	struct BlockIteratorState
	{
		Index stride;
		Index span;
		Index size;
		Index count;
	};

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock block(TensorBlockDesc& desc,
															TensorBlockScratch& scratch,
															bool /*root_of_expr_ast*/ = false) const
	{
		static const bool is_col_major = static_cast<int>(Layout) == static_cast<int>(ColMajor);

		// Compute spatial coordinates for the first block element.
		array<Index, NumDims> coords;
		extract_coordinates(desc.offset(), coords);
		array<Index, NumDims> initial_coords = coords;

		// Offset in the output block buffer.
		Index offset = 0;

		// Initialize output block iterator state. Dimension in this array are
		// always in inner_most -> outer_most order (col major layout).
		array<BlockIteratorState, NumDims> it;
		for (int i = 0; i < NumDims; ++i) {
			const int dim = is_col_major ? i : NumDims - 1 - i;
			it[i].size = desc.dimension(dim);
			it[i].stride = i == 0 ? 1 : (it[i - 1].size * it[i - 1].stride);
			it[i].span = it[i].stride * (it[i].size - 1);
			it[i].count = 0;
		}
		eigen_assert(it[0].stride == 1);

		// Prepare storage for the materialized generator result.
		const typename TensorBlock::Storage block_storage = TensorBlock::prepareStorage(desc, scratch);

		CoeffReturnType* block_buffer = block_storage.data();

		static const int packet_size = PacketType<CoeffReturnType, Device>::size;

		static const int inner_dim = is_col_major ? 0 : NumDims - 1;
		const Index inner_dim_size = it[0].size;
		const Index inner_dim_vectorized = inner_dim_size - packet_size;

		while (it[NumDims - 1].count < it[NumDims - 1].size) {
			Index i = 0;
			// Generate data for the vectorized part of the inner-most dimension.
			for (; i <= inner_dim_vectorized; i += packet_size) {
				for (Index j = 0; j < packet_size; ++j) {
					array<Index, NumDims> j_coords = coords; // Break loop dependence.
					j_coords[inner_dim] += j;
					*(block_buffer + offset + i + j) = m_generator(j_coords);
				}
				coords[inner_dim] += packet_size;
			}
			// Finalize non-vectorized part of the inner-most dimension.
			for (; i < inner_dim_size; ++i) {
				*(block_buffer + offset + i) = m_generator(coords);
				coords[inner_dim]++;
			}
			coords[inner_dim] = initial_coords[inner_dim];

			// For the 1d tensor we need to generate only one inner-most dimension.
			if (NumDims == 1)
				break;

			// Update offset.
			for (i = 1; i < NumDims; ++i) {
				if (++it[i].count < it[i].size) {
					offset += it[i].stride;
					coords[is_col_major ? i : NumDims - 1 - i]++;
					break;
				}
				if (i != NumDims - 1)
					it[i].count = 0;
				coords[is_col_major ? i : NumDims - 1 - i] = initial_coords[is_col_major ? i : NumDims - 1 - i];
				offset -= it[i].span;
			}
		}

		return block_storage.AsTensorMaterializedBlock();
	}

	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool) const
	{
		// TODO(rmlarsen): This is just a placeholder. Define interface to make
		// generators return their cost.
		return TensorOpCost(0, 0, TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>());
	}

	EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }

#ifdef EIGEN_USE_SYCL
	// binding placeholder accessors to a command group handler for SYCL
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler&) const {}
#endif

  protected:
	EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void extract_coordinates(Index index, array<Index, NumDims>& coords) const
	{
		if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
			for (int i = NumDims - 1; i > 0; --i) {
				const Index idx = index / m_fast_strides[i];
				index -= idx * m_strides[i];
				coords[i] = idx;
			}
			coords[0] = index;
		} else {
			for (int i = 0; i < NumDims - 1; ++i) {
				const Index idx = index / m_fast_strides[i];
				index -= idx * m_strides[i];
				coords[i] = idx;
			}
			coords[NumDims - 1] = index;
		}
	}

	const Device EIGEN_DEVICE_REF m_device;
	Dimensions m_dimensions;
	array<Index, NumDims> m_strides;
	array<IndexDivisor, NumDims> m_fast_strides;
	Generator m_generator;
};

} // end namespace Eigen

#endif // EIGEN_CXX11_TENSOR_TENSOR_GENERATOR_H
